title: Frequency recognition methods for dual-frequency SSVEP based brain-computer interface.
abstract: Dual-frequency steady-state visual evoked potential (SSVEP) was suggested to generate more stimuli using a few flickering frequencies for brain-computer interface. Dual--frequency SSVEP peaks at more than two frequencies-both main and harmonic frequencies. However multi-frequency recognition strategy has not been investigated for dual-frequency SSVEP. In this paper, three modified power spectral density analysis (PSDA) methods and two modified canonical correlation analysis (CCA) methods were tested for dual-frequency SSVEP classification. Three methods among the five methods used conventional features or classification techniques, and the other two methods used modified features for harmonic frequencies. As a result, CCA with novel features showed the best BCI performance. Also the use of harmonic frequencies improved BCI performance of dual-frequency SSVEP.
authors.forename: Min Hye
authors.initials: MH
authors.lastname: Chang
authors.forename: Kwang Suk
authors.initials: KS
authors.lastname: Park
mesh_terms: D000465:Algorithms; D062207:Brain-Computer Interfaces; D004569:Electroencephalography / Q000379:methods*; D005074:Evoked Potentials, Visual / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D010363:Pattern Recognition, Automated